Zone-Based Indoor Localization Using Neural Networks: A View from a Real Testbed

Anzum, Nafisa; Afroze, Syeda Farzia; Rahman, Ashikur

doi:10.1109/icc.2018.8422182

Cited by 20 publications

(11 citation statements)

References 13 publications

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“…This scheme uses a deep learning model of a stacked denosing auto encoder and a probability architecture to process the inference for the received WiFi signal and find the relationship between the WiFi signal heard by the mobile device and its position. Anzum et al [6] proposed a zonebased indoor positioning method using counter propagation neural networks (CPN). When the traditional CPN is applied, many empty clusters are generated.…”

Section: Fingerprint-based Localizationmentioning

confidence: 99%

“…With the popularity of wireless LANs and wireless access devices, WiFi-based indoor localization has become more and more popular in recent years. Based on the WiFi localization systems, fingerprint schemes [5][6][7][8][9][10] show great advantages and accuracy in indoor localization with RSS and SNR signals. In the offline phase, select multiple physical locations in the region of interest and the signals received from APs built in indoor environment are defined as labeled data in these selected locations.…”

Section: Introductionmentioning

confidence: 99%

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A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

Chen

Hsu

Chung

2022

Sensors

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Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.

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Section: Fingerprint-based Localizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

Chen

Hsu

Chung

2022

Sensors

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“…Existing localization approaches have been explicitly designed to solve certain localization tasks that vary in the granularity of the estimated position. Solutions range from building/floor distinction [4], [15], to classifying predetermined zones/areas [16], [17], whereas some models are capable of estimating location coordinates (mostly 2D) [5] or predicting trajectories [18].…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Neural Network for Position Estimation in Large-Scale Indoor Environments

Laska

Blankenbach

2022

IEEE Access

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Pedestrian localization within large-scale multi-building/multi-floor indoor environments remains a challenging task. Fingerprinting-based approaches are particularly suited for such large-scale deployments due to their low requirements of hardware installments. Recently, the fingerprinting problem has been addressed by deep learning. Existing models are mostly task specific by providing floor classification or position estimation within a small area. A strategy to support localization within large-scale environments is to sequentially apply hierarchical models. This has several drawbacks including missing scalability and increased deployment complexity on smartphones. We propose a unifying approach based on training a single neural network that classifies the building/floor and predicts the position in a single forward-pass of the network. Our model classifies a grid cell and performs within grid cell regression, which solves the performance degradation of applying regression within large areas. To reduce the error in case of misclassified grid cells, we propose a novel technique called multi cell encoding learning (multi-CEL), where a model simultaneously learns several redundant position representations within an overlapping grid cell encoding. For three public WLAN fingerprinting datasets, we demonstrate that multi-CEL surpasses existing state-of-the-art multi-task learning neural networks and even outperforms regression neural networks explicitly trained for 2D-positioning by up to 17%.

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“…Furthermore, this work employed samples acquired only on two days and, therefore, did not evaluate the evolution of the different IPSs over the time. Another interesting approach is in [ 22 ]. The authors proposed a zone-level IPS by applying neural network techniques, and they compared it against the KNN algorithm, achieving a slight change in accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System

López-Pastor

Ruiz-Ruiz²,

García-Sánchez

et al. 2021

Sensors

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A complete contextual marketing platform including an indoor positioning system (IPS) for smartphones is proposed and evaluated to later be deployed in large infrastructures, such as malls. To this end, we design and implement a novel methodology based on location-as-a-service (LAAS), comprising all the required phases of IPS generation: mall digital map creation, the tools/procedures for offline calibration fingerprint acquisition, the location algorithm, the smartphone app acquiring the fingerprint data, and a validation procedure. To select an appropriate fingerprint location algorithm, a comparison among K-nearest neighbors (KNN), support vector machine (SVM), and Freeloc is accomplished by employing a set of different smartphones in two malls and assessing different occupancy levels. We demonstrate that our solution can be quickly deployed at shop level accuracy in any new location, resulting in a robust and scalable proposal.

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Zone-Based Indoor Localization Using Neural Networks: A View from a Real Testbed

Cited by 20 publications

References 13 publications

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

Multi-Task Neural Network for Position Estimation in Large-Scale Indoor Environments

An Automatized Contextual Marketing System Based on a Wi-Fi Indoor Positioning System

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